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| import gradio as gr | |
| import jax | |
| import numpy as np | |
| import jax.numpy as jnp | |
| from flax.jax_utils import replicate | |
| from flax.training.common_utils import shard | |
| from PIL import Image | |
| from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel | |
| import cv2 | |
| import os | |
| def load_image(image): | |
| if isinstance(image, str): | |
| if image.startswith("http://") or image.startswith("https://"): | |
| image = PIL.Image.open(requests.get(image, stream=True).raw) | |
| elif os.path.isfile(image): | |
| image = PIL.Image.open(image) | |
| elif isinstance(image, PIL.Image.Image): | |
| image = image | |
| image = PIL.ImageOps.exif_transpose(image) | |
| image = image.convert("RGB") | |
| return image | |
| def image_grid(imgs, rows, cols): | |
| w, h = imgs[0].size | |
| grid = Image.new("RGB", size=(cols * w, rows * h)) | |
| for i, img in enumerate(imgs): | |
| grid.paste(img, box=(i % cols * w, i // cols * h)) | |
| return grid | |
| def create_key(seed=0): | |
| return jax.random.PRNGKey(seed) | |
| rng = create_key(0) | |
| def canny_filter(image): | |
| gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0) | |
| edges_image = cv2.Canny(blurred_image, 50, 150) | |
| return edges_image | |
| def infer(prompts, negative_prompts, image): | |
| # load control net and stable diffusion v1-5 | |
| controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( | |
| "jax-diffusers-event/canny-coyo1m", dtype=jnp.float32 | |
| ) | |
| pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", controlnet=controlnet, from_pt=True, dtype=jnp.float32 | |
| ) | |
| params["controlnet"] = controlnet_params | |
| num_samples = jax.device_count() | |
| rng = jax.random.split(rng, jax.device_count()) | |
| canny_image = canny_filter(image) | |
| prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples) | |
| negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples) | |
| processed_image = pipe.prepare_image_inputs([canny_image] * num_samples) | |
| p_params = replicate(params) | |
| prompt_ids = shard(prompt_ids) | |
| negative_prompt_ids = shard(negative_prompt_ids) | |
| processed_image = shard(processed_image) | |
| output = pipe( | |
| prompt_ids=prompt_ids, | |
| image=processed_image, | |
| params=p_params, | |
| prng_seed=rng, | |
| num_inference_steps=50, | |
| neg_prompt_ids=negative_prompt_ids, | |
| jit=True, | |
| ).images | |
| output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:]))) | |
| output_images = image_grid(output_images, num_samples // 4, 4) | |
| return output_images | |
| gr.Interface(infer, inputs=["text", "text", "image"], outputs="gallery").launch() | |